Leveraging machine learning for personalised learning, automated feedback, and predictive analytics in college English education

Machine learning is changing the face of college English education with personalisation, immediate feedback, and student performance prediction. In this study, we propose an ML-driven framework. The first stage is an RNN attention model-based content recommendation, followed by a fine-tuned GPT-4 model for writing evaluation and an optimised random forest model for early risk detection. On real-world data, we achieve a 31.2% increase in recommendation accuracy, a 49% reduction in grading time with high BLEU and ROUGE scores, and 88.3% accuracy in identifying at-risk students. If data privacy, ethics, and the like are appropriately managed, ML increases student performance by 27% and grading efficiency by 40%.